5063 Final Proj
Yun Yee Tan
4/5/2021
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.3 ✓ purrr 0.3.4
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library(plotly)##
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## last_plot## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layoutdssi <- read_csv("dssi_2020.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## `Country Name` = col_character(),
## `Country Code` = col_character(),
## `Counterpart-Area Name` = col_character(),
## `Counterpart-Area Code` = col_character(),
## `Series Name` = col_character(),
## `Series Code` = col_character(),
## `2000` = col_character(),
## `2008` = col_character(),
## `2019` = col_character(),
## `2020` = col_character()
## )Data source: https://databank.worldbank.org/source/international-debt-statistics:-dssi
Georgetown report: http://gppreview.com/2021/02/10/international-debt-exploring-new-data-world-bank/
“In 2020, the World Bank – reacting to global financial shocks from COVID-19 and emphasizing debt transparency – for the first time released individual creditor countries within “International Debt Statistics.” The interactive map below visualizes these newly released creditor country breakdowns. Previously, such data was only available at a generalized “creditor region” level. Such a release provides a tremendous level of granularity, which economic and public policy analysts can use to examine relationships between the 120 low and middle-income countries of the world."
World Bank report: https://openknowledge.worldbank.org/bitstream/handle/10986/34588/9781464816109.pdf
About the DSSI:
https://www.worldbank.org/en/topic/debt/brief/covid-19-debt-service-suspension-initiative
In all, 73 countries are eligible for a temporary suspension of debt-service payments owed to their official bilateral creditors. The G20 has also called on private creditors to participate in the initiative on comparable terms. The suspension period, originally set to end on December 31, 2020, has been extended through June 2021.
Net inflows from the World Bank (International Bank for Reconstruction and Development [IBRD] and International Development Association [IDA])
“Estimates are derived from monthly IDS projections based on end-2019 external public and publicly guaranteed debt outstanding and disbursed.”
2020 data only available for debt service and principal repayments…
Cleaning the data:
dssi <- dssi %>% select(-`Series Code`) %>% gather("Year", "Value", 6:9)
dssi <- dssi %>% anti_join(dssi[c(29785, 29786, 29787, 29788, 29789, 59574, 59575, 59576, 59577, 59578, 89363, 89364, 89365, 89366, 89367, 119152, 119153, 119154, 119155, 119156),])## Joining, by = c("Country Name", "Country Code", "Counterpart-Area Name", "Counterpart-Area Code", "Series Name", "Year", "Value")dssi <- dssi %>% spread(`Series Name`, `Value`)
colnames(dssi) <- c("Debtor", "Debtor_Code", "Creditor", "Creditor_Code", "Year", "Debt_Forgiveness", "Debt_Stocks")
dssi$Debt_Forgiveness <- as.numeric(dssi$Debt_Forgiveness)## Warning: NAs introduced by coerciondssi$Debt_Stocks <- as.numeric(dssi$Debt_Stocks)## Warning: NAs introduced by coerciondssi$Year <- as.factor(dssi$Year)
dssi[is.na(dssi)] <- 0
dssi$Debtor <- stringr::str_trim(dssi$Debtor, side = "both")
dssi$Creditor <- stringr::str_trim(dssi$Creditor, side = "both")
dssi <- dssi %>% mutate(Debtor_Region = case_when(
Debtor %in% c("Angola", "Benin", "Burkina Faso", "Burundi", "Cabo Verde", "Cameroon", "Central African Republic", "Chad", "Comoros", "Congo, Dem. Rep.", "Congo, Rep.", "Cote d'Ivoire", "Djibouti", "Ethiopia", "Gambia, The", "Ghana", "Guinea", "Guinea-Bissau", "Kenya", "Lesotho", "Liberia", "Madagascar", "Malawi", "Mali", "Mauritania", "Mozambique", "Niger", "Nigeria", "Rwanda", "Sao Tome and Principe", "Senegal", "Sierra Leone", "Somalia", "Tanzania", "Togo", "Uganda", "Zambia") ~ "Africa",
Debtor %in% c("Bangladesh", "Bhutan", "Cambodia", "Kyrgyz Republic", "Lao PDR", "Maldives", "Mongolia", "Myanmar", "Nepal", "Pakistan", "Tajikistan", "Timor-Leste", "Uzbekistan") ~ "Asia",
Debtor %in% c("Afghanistan", "Yemen, Rep.") ~ "Middle East",
Debtor %in% c("Guyana", "Haiti", "Honduras", "Nicaragua") ~ "South & Central America",
Debtor %in% c("Dominica", "Grenada", "St. Lucia", "St. Vincent and the Grenadines") ~ "The Caribbeans",
Debtor %in% c("Fiji", "Papua New Guinea", "Samoa", "Solomon Islands", "Tonga", "Vanuatu") ~ "Pacific Islands",
Debtor %in% c("Kosovo", "Moldova") ~ "Europe"
))Debtor countries:
dssi$Debtor %>% unique()## [1] "Afghanistan" "Angola"
## [3] "Bangladesh" "Benin"
## [5] "Bhutan" "Burkina Faso"
## [7] "Burundi" "Cabo Verde"
## [9] "Cambodia" "Cameroon"
## [11] "Central African Republic" "Chad"
## [13] "Comoros" "Congo, Dem. Rep."
## [15] "Congo, Rep." "Cote d'Ivoire"
## [17] "Djibouti" "Dominica"
## [19] "Ethiopia" "Fiji"
## [21] "Gambia, The" "Ghana"
## [23] "Grenada" "Guinea"
## [25] "Guinea-Bissau" "Guyana"
## [27] "Haiti" "Honduras"
## [29] "Kenya" "Kosovo"
## [31] "Kyrgyz Republic" "Lao PDR"
## [33] "Lesotho" "Liberia"
## [35] "Madagascar" "Malawi"
## [37] "Maldives" "Mali"
## [39] "Mauritania" "Moldova"
## [41] "Mongolia" "Mozambique"
## [43] "Myanmar" "Nepal"
## [45] "Nicaragua" "Niger"
## [47] "Nigeria" "Pakistan"
## [49] "Papua New Guinea" "Rwanda"
## [51] "Samoa" "Sao Tome and Principe"
## [53] "Senegal" "Sierra Leone"
## [55] "Solomon Islands" "Somalia"
## [57] "St. Lucia" "St. Vincent and the Grenadines"
## [59] "Tajikistan" "Tanzania"
## [61] "Timor-Leste" "Togo"
## [63] "Tonga" "Uganda"
## [65] "Uzbekistan" "Vanuatu"
## [67] "Yemen, Rep." "Zambia"68 in total.
dssi %>% select(Debtor, Debtor_Region) %>% unique() %>% group_by(Debtor_Region) %>% count() %>% arrange(desc(n))## # A tibble: 7 x 2
## # Groups: Debtor_Region [7]
## Debtor_Region n
## <chr> <int>
## 1 Africa 37
## 2 Asia 13
## 3 Pacific Islands 6
## 4 South & Central America 4
## 5 The Caribbeans 4
## 6 Europe 2
## 7 Middle East 2Most debtor countries are from Africa, followed by Asia.
Creditors:
dssi$Creditor %>% unique()## [1] "Afghanistan" "African Dev. Bank"
## [3] "Albania" "Algeria"
## [5] "Andorra" "Angola"
## [7] "Anguilla" "Antigua"
## [9] "Argentina" "Armenia"
## [11] "Aruba" "Asian Dev. Bank"
## [13] "Australia" "Austria"
## [15] "Azerbaijan" "Bahamas"
## [17] "Bahrain" "Bangladesh"
## [19] "Barbados" "Belarus"
## [21] "Belgium" "Belize"
## [23] "Benin" "Bermuda"
## [25] "Bhutan" "Bolivia"
## [27] "Bondholders" "Bosnia-Herzegovina"
## [29] "Botswana" "Brazil"
## [31] "Brunei" "Bulgaria"
## [33] "Burkina Faso" "Burundi"
## [35] "Cabo Verde" "Cambodia"
## [37] "Cameroon" "Canada"
## [39] "Cayman Islands" "Central African Republic"
## [41] "Chad" "Chile"
## [43] "China" "Colombia"
## [45] "Comoros" "Congo, Dem. Rep."
## [47] "Congo, Rep." "Costa Rica"
## [49] "Cote D`Ivoire, Republic Of" "Croatia"
## [51] "Cuba" "Cyprus"
## [53] "Czech Republic" "Czechoslovakia"
## [55] "Denmark" "Djibouti"
## [57] "Dominica" "Dominican Republic"
## [59] "Ecuador" "Egypt"
## [61] "El Salvador" "Equatorial Guinea"
## [63] "Eritrea" "Estonia"
## [65] "Eswatini" "Ethiopia"
## [67] "Fiji" "Finland"
## [69] "France" "French Polynesia"
## [71] "Gabon" "Gambia, The"
## [73] "Georgia" "German Dem. Rep."
## [75] "Germany, Fed.Rep. Of" "Ghana"
## [77] "Gibraltar" "Greece"
## [79] "Grenada" "Guadeloupe"
## [81] "Guam" "Guatemala"
## [83] "Guinea" "Guinea-Bissau"
## [85] "Guyana" "Haiti"
## [87] "Honduras" "Hong Kong"
## [89] "Hungary" "Iceland"
## [91] "India" "Indonesia"
## [93] "Inter-American Dev. Bank" "International Monetary Fund"
## [95] "Iran, Islamic Republic Of" "Iraq"
## [97] "Ireland" "Israel"
## [99] "Italy" "Jamaica"
## [101] "Japan" "Jordan"
## [103] "Kazakhstan" "Kenya"
## [105] "Kiribati" "Korea, D.P.R. Of"
## [107] "Korea, Republic Of" "Kosovo"
## [109] "Kuwait" "Kyrgyz Republic"
## [111] "Lao Peoples Democratic Repub." "Latvia"
## [113] "Lebanon" "Lesotho"
## [115] "Liberia" "Libya"
## [117] "Lithuania" "Luxembourg"
## [119] "Macao" "Madagascar"
## [121] "Malawi" "Malaysia"
## [123] "Maldives" "Mali"
## [125] "Malta" "Mauritania"
## [127] "Mauritius" "Mexico"
## [129] "Micronesia Fed Sts" "Moldova"
## [131] "Monaco" "Mongolia"
## [133] "Montenegro" "Morocco"
## [135] "Mozambique" "Multiple Lenders"
## [137] "Myanmar" "Namibia"
## [139] "Nepal" "Neth. Antilles"
## [141] "Netherlands" "New Caledonia (Fr.)"
## [143] "New Zealand" "Nicaragua"
## [145] "Niger" "Nigeria"
## [147] "North Macedonia" "Norway"
## [149] "Oman" "Other Bilateral"
## [151] "Other Multilaterals" "Other Multiple Lenders"
## [153] "Pacific Is. (Us)" "Pakistan"
## [155] "Panama" "Papua New Guinea"
## [157] "Paraguay" "Peru"
## [159] "Philippines" "Poland"
## [161] "Portugal" "Puerto Rico"
## [163] "Qatar" "Reunion"
## [165] "Romania" "Russian Federation"
## [167] "Rwanda" "Samoa"
## [169] "Sao Tome & Principe" "Saudi Arabia"
## [171] "Senegal" "Serbia"
## [173] "Seychelles" "Sierra Leone"
## [175] "Singapore" "Slovak Republic"
## [177] "Slovenia" "Solomon Islands"
## [179] "Somalia" "South Africa"
## [181] "Spain" "Sri Lanka"
## [183] "St. Kitts And Nevis" "St. Lucia"
## [185] "St. Vincent & The Grenadines" "Sudan"
## [187] "Surinam" "Sweden"
## [189] "Switzerland" "Syrian Arab Republic"
## [191] "Tajikistan" "Tanzania"
## [193] "Thailand" "Timor-Leste"
## [195] "Togo" "Tonga"
## [197] "Trinidad & Tobago" "Tunisia"
## [199] "Turkey" "Turkmenistan"
## [201] "Uganda" "Ukraine"
## [203] "United Arab Emirates" "United Kingdom"
## [205] "United States" "Uruguay"
## [207] "USSR" "Uzbekistan"
## [209] "Vanuatu" "Venezuela, Republic Bolivarian"
## [211] "Vietnam" "Virgin Is.(US)"
## [213] "World" "World Bank-IBRD"
## [215] "World Bank-IDA" "Yemen, Republic Of"
## [217] "Yugoslavia" "Zambia"
## [219] "Zimbabwe"Includes both countries and multilateral organizations.
Overall debt levels
dssi %>% filter(Creditor == "World") %>% filter(Year %in% c(2000, 2019)) %>% group_by(Year) %>% summarize(global_debt_bil = sum(Debt_Stocks)/10^9)## # A tibble: 2 x 2
## Year global_debt_bil
## <fct> <dbl>
## 1 2000 262.
## 2 2019 744.Total global debt increased by more than 180% from US$261 billion in 2000 to US\(744 billion in 2019. This is denominated in current US\) (2021) and hence accounts for inflationary effects.
Debt levels
ggplot(dssi %>% filter(Year != 2020)) + geom_boxplot(aes(as.factor(Year), Debt_Stocks/10^9)) + labs(x = "Year", y = "External debt level (billions)", title = "External debt stocks over time")
debt_2000 <- dssi %>% filter(Year == 2000)
debt_2019 <- dssi %>% filter(Year == 2019)debt_2000 %>% filter(Creditor == "World") %>% mutate(Debt_Stocks_Bil = Debt_Stocks/10^9) %>% select(Debtor, Creditor, Debt_Stocks_Bil) %>% arrange(desc(Debt_Stocks_Bil))## # A tibble: 68 x 3
## Debtor Creditor Debt_Stocks_Bil
## <chr> <chr> <dbl>
## 1 Nigeria World 33.5
## 2 Pakistan World 33.1
## 3 Bangladesh World 15.6
## 4 Cote d'Ivoire World 12.1
## 5 Congo, Dem. Rep. World 11.8
## 6 Cameroon World 10.6
## 7 Angola World 9.76
## 8 Tanzania World 7.19
## 9 Nicaragua World 6.82
## 10 Ghana World 6.74
## # … with 58 more rowsdebt_2019 %>% filter(Creditor == "World") %>% mutate(Debt_Stocks_Bil = Debt_Stocks/10^9) %>% select(Debtor, Creditor, Debt_Stocks_Bil) %>% arrange(desc(Debt_Stocks_Bil))## # A tibble: 68 x 3
## Debtor Creditor Debt_Stocks_Bil
## <chr> <chr> <dbl>
## 1 Pakistan World 101.
## 2 Bangladesh World 57.1
## 3 Nigeria World 54.8
## 4 Angola World 52.0
## 5 Kenya World 34.2
## 6 Mongolia World 31.4
## 7 Ethiopia World 28.3
## 8 Zambia World 27.3
## 9 Ghana World 27.0
## 10 Uzbekistan World 21.7
## # … with 58 more rows2000: Nigeria ($33.5 bil), Pakistan ($33.9 bil), Bangladesh ($15.6 bil), Cote d’Ivoire ($12.1 bil), Angola was ranked 7th at $9.8 bil.
2019: Pakistan $100 billion followed by Bangladesh ($57 bil), Nigeria ($54 bil), Angola ($51 bil).
debt_2000 %>% group_by(Debtor_Region) %>% filter(Creditor == "World") %>% summarize(total_debt_bil = sum(Debt_Stocks)/10^9, avg_debt_bil = mean(Debt_Stocks)/10^9) %>% arrange(desc(total_debt_bil))## # A tibble: 7 x 3
## Debtor_Region total_debt_bil avg_debt_bil
## <chr> <dbl> <dbl>
## 1 Africa 164. 4.43
## 2 Asia 72.1 5.55
## 3 South & Central America 15.0 3.75
## 4 Middle East 5.15 2.58
## 5 Pacific Islands 2.93 0.488
## 6 Europe 1.91 0.957
## 7 The Caribbeans 0.800 0.200debt_2019 %>% group_by(Debtor_Region) %>% filter(Creditor == "World") %>% summarize(total_debt_bil = sum(Debt_Stocks)/10^9, avg_debt_bil = mean(Debt_Stocks)/10^9) %>% arrange(desc(total_debt_bil))## # A tibble: 7 x 3
## Debtor_Region total_debt_bil avg_debt_bil
## <chr> <dbl> <dbl>
## 1 Africa 395. 10.7
## 2 Asia 281. 21.6
## 3 South & Central America 25.3 6.32
## 4 Pacific Islands 21.1 3.52
## 5 Europe 9.96 4.98
## 6 Middle East 9.72 4.86
## 7 The Caribbeans 1.83 0.457Debt timeseries
debt_series <- read_csv("debt_series.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_character(),
## `2012 [YR2012]` = col_double(),
## `2013 [YR2013]` = col_double(),
## `2014 [YR2014]` = col_double(),
## `2015 [YR2015]` = col_double(),
## `2016 [YR2016]` = col_double(),
## `2017 [YR2017]` = col_double(),
## `2018 [YR2018]` = col_double(),
## `2019 [YR2019]` = col_double()
## )
## ℹ Use `spec()` for the full column specifications.debt_series <- debt_series[1:68, c(1, 7:26)]
colnames(debt_series) <- c("Debtor", 2000:2019)
debt_series <- debt_series %>% gather(key = "Year", value = "Debt_Stocks", 2:21)
debt_series$Debt_Stocks <- as.numeric(debt_series$Debt_Stocks)## Warning: NAs introduced by coerciondebt_series$Year <- as.numeric(debt_series$Year)
debt_series[which(is.na(debt_series$Debt_Stocks)), "Debt_Stocks"] <- 0
debt_series <- debt_series %>% mutate(Debtor_Region = case_when(
Debtor %in% c("Angola", "Benin", "Burkina Faso", "Burundi", "Cabo Verde", "Cameroon", "Central African Republic", "Chad", "Comoros", "Congo, Dem. Rep.", "Congo, Rep.", "Cote d'Ivoire", "Djibouti", "Ethiopia", "Gambia, The", "Ghana", "Guinea", "Guinea-Bissau", "Kenya", "Lesotho", "Liberia", "Madagascar", "Malawi", "Mali", "Mauritania", "Mozambique", "Niger", "Nigeria", "Rwanda", "Sao Tome and Principe", "Senegal", "Sierra Leone", "Somalia", "Tanzania", "Togo", "Uganda", "Zambia") ~ "Africa",
Debtor %in% c("Bangladesh", "Bhutan", "Cambodia", "Kyrgyz Republic", "Lao PDR", "Maldives", "Mongolia", "Myanmar", "Nepal", "Pakistan", "Tajikistan", "Timor-Leste", "Uzbekistan") ~ "Asia",
Debtor %in% c("Afghanistan", "Yemen, Rep.") ~ "Middle East",
Debtor %in% c("Guyana", "Haiti", "Honduras", "Nicaragua") ~ "South & Central America",
Debtor %in% c("Dominica", "Grenada", "St. Lucia", "St. Vincent and the Grenadines") ~ "The Caribbeans",
Debtor %in% c("Fiji", "Papua New Guinea", "Samoa", "Solomon Islands", "Tonga", "Vanuatu") ~ "Pacific Islands",
Debtor %in% c("Kosovo", "Moldova") ~ "Europe"
))ggplotly(debt_series %>% group_by(Debtor_Region, Year) %>% summarize(total_debt_bil = sum(Debt_Stocks)/10^9, mean_debt_bil = mean(Debt_Stocks)/10^9) %>% ggplot() + geom_line(aes(x = Year, y = total_debt_bil, color = Debtor_Region)))## `summarise()` has grouped output by 'Debtor_Region'. You can override using the `.groups` argument.ggplotly(debt_series %>% group_by(Debtor_Region, Year) %>% summarize(total_debt_bil = sum(Debt_Stocks)/10^9, mean_debt_bil = mean(Debt_Stocks)/10^9) %>% ggplot() + geom_line(aes(x = Year, y = mean_debt_bil, color = Debtor_Region)))## `summarise()` has grouped output by 'Debtor_Region'. You can override using the `.groups` argument.Total debt for Africa and Asia has risen drastically compared to other regions (I can further calculate % changes from t=0)
Asia has the highest mean debt per country, and this has increased at a greater pace than the other regions
Creditors
2000:
debt_2000 %>% group_by(Creditor) %>% summarize(total_lending_bil = sum(Debt_Stocks)/10^9) %>% arrange(desc(total_lending_bil))## # A tibble: 219 x 2
## Creditor total_lending_bil
## <chr> <dbl>
## 1 World 262.
## 2 World Bank-IDA 49.0
## 3 Multiple Lenders 26.8
## 4 Other Multiple Lenders 21.7
## 5 Japan 20.5
## 6 Asian Dev. Bank 14.0
## 7 France 13.9
## 8 International Monetary Fund 12.6
## 9 Russian Federation 11.4
## 10 African Dev. Bank 10.9
## # … with 209 more rowsTop country creditors: Japan, France, Russian Federation, US, Germany, Italy, UK, China
2019:
debt_2019 %>% group_by(Creditor) %>% summarize(total_lending_bil = sum(Debt_Stocks)/10^9) %>% arrange(desc(total_lending_bil))## # A tibble: 219 x 2
## Creditor total_lending_bil
## <chr> <dbl>
## 1 World 744.
## 2 Other Multiple Lenders 203.
## 3 World Bank-IDA 111.
## 4 China 109.
## 5 Bondholders 76.3
## 6 Asian Dev. Bank 36.2
## 7 International Monetary Fund 32.4
## 8 Other Multilaterals 31.2
## 9 Japan 24.1
## 10 African Dev. Bank 23.1
## # … with 209 more rows19 years later, China is now the top creditor country. India and Saudi Arabia have also emerged, ranking above the US and Germany.
Pie Charts~
library(highcharter) ## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoocols <- RColorBrewer::brewer.pal(8, "Dark2")
top_creditors_2000 <- debt_2000 %>% filter(!Creditor %in% c("World", "Multiple Lenders", "Other Multiple Lenders", "Other Multilaterals", "Bondholders", "Other Bilateral")) %>% group_by(Creditor) %>% summarize(total_debt_bil = sum(Debt_Stocks)/10^9) %>% mutate(pct_global_debt = total_debt_bil/261.6634) %>% arrange(desc(total_debt_bil)) %>% head(n = 20)
top_creditors_2019 <- debt_2019 %>% filter(!Creditor %in% c("World", "Multiple Lenders", "Other Multiple Lenders", "Other Multilaterals", "Bondholders", "Other Bilateral")) %>% group_by(Creditor) %>% summarize(total_debt_bil = sum(Debt_Stocks)/10^9) %>% mutate(pct_global_debt = total_debt_bil/744.4824) %>% arrange(desc(total_debt_bil)) %>% head(n = 20)
top_creditors_2000 %>% hchart("pie", hcaes(x = Creditor, y = total_debt_bil), name = "Total Lending (billions of US$)") %>% hc_title(text = "Top creditors in 2000") %>% hc_subtitle(text = "subtitle") %>% hc_caption(text = "Data: The World Bank (2021)") %>% hc_colors(cols)top_creditors_2019 %>% hchart("pie", hcaes(x = Creditor, y = total_debt_bil), name = "Total Lending (billions of US$)") %>% hc_title(text = "Top creditors in 2019") %>% hc_subtitle(text = "subtitle") %>% hc_caption(text = "Data: The World Bank (2021)") %>% hc_colors(cols)## Is there a way to highlight China?# or maybe I should choose top creditors based on total across the years
top_creditors <- top_creditors_2000 %>% pull(Creditor) %>% append(top_creditors_2019 %>% pull(Creditor)) %>% unique()
top_creditor_stats <- dssi %>% filter(Creditor %in% top_creditors) %>% filter(Year %in% c(2000, 2019)) %>% group_by(Creditor, Year) %>% summarize(total_lending_bil = sum(Debt_Stocks)/10^9, total_forgiveness_bil = sum(Debt_Forgiveness)/10^9)## `summarise()` has grouped output by 'Creditor'. You can override using the `.groups` argument.top_creditor_stats$pct_global_debt <- ifelse(top_creditor_stats$Year == 2000, top_creditor_stats$total_lending_bil/261.6634, top_creditor_stats$total_lending_bil/744.4824)
ggplot(top_creditor_stats) + geom_col(aes(x = reorder(Creditor, total_lending_bil), y = total_lending_bil, fill = as.factor(Year)), position = "dodge") + coord_flip()
ggplot(top_creditor_stats) + geom_col(aes(x = reorder(Creditor, pct_global_debt), y = pct_global_debt, fill = as.factor(Year)), position = "dodge") + coord_flip() + theme_minimal()
## How do I show 2000 before 2019?As a percentage: China has increased dramatically. International banks including the World Bank, ADB, IMF, African Development Bank have all decreased, as with most other countries. Stark decrease in France, Russia, US, Germany, Japan.
Selected Creditors over time (those with drastic changes, top creditors)
Data cleaning:
selected_creditors <- read_csv("selected_creditors.csv")##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_character()
## )
## ℹ Use `spec()` for the full column specifications.selected_creditors <- selected_creditors %>% select(-`Series Code`)
colnames(selected_creditors) <- c("Debtor", "Debtor_Code", "Creditor", "Creditor_Code", "Series", 2000:2019)
selected_creditors <- selected_creditors %>% gather("year", "value", 6:25)
selected_creditors[which(selected_creditors$Series == "External debt stocks, total (DOD, current US$)"), "Series"] <- "Debt_Stocks"
selected_creditors[which(selected_creditors$Series == "Debt forgiveness or reduction (current US$)"), "Series"] <- "Debt_Forgiveness"
selected_creditors <- selected_creditors %>% filter(!is.na(Series))
selected_creditors <- spread(selected_creditors, Series, value)
selected_creditors$Debt_Forgiveness <- as.numeric(selected_creditors$Debt_Forgiveness)## Warning: NAs introduced by coercionselected_creditors$Debt_Stocks <- as.numeric(selected_creditors$Debt_Stocks)## Warning: NAs introduced by coercionselected_creditors$year <- as.numeric(selected_creditors$year)
selected_creditors[is.na(selected_creditors)] <- 0
selected_creditors$Debtor <- stringr::str_trim(selected_creditors$Debtor, side = "both")
selected_creditors$Creditor <- stringr::str_trim(selected_creditors$Creditor, side = "both")
selected_creditors <- selected_creditors %>% mutate(Debtor_Region = case_when(
Debtor %in% c("Angola", "Benin", "Burkina Faso", "Burundi", "Cabo Verde", "Cameroon", "Central African Republic", "Chad", "Comoros", "Congo, Dem. Rep.", "Congo, Rep.", "Cote d'Ivoire", "Djibouti", "Ethiopia", "Gambia, The", "Ghana", "Guinea", "Guinea-Bissau", "Kenya", "Lesotho", "Liberia", "Madagascar", "Malawi", "Mali", "Mauritania", "Mozambique", "Niger", "Nigeria", "Rwanda", "Sao Tome and Principe", "Senegal", "Sierra Leone", "Somalia", "Tanzania", "Togo", "Uganda", "Zambia") ~ "Africa",
Debtor %in% c("Bangladesh", "Bhutan", "Cambodia", "Kyrgyz Republic", "Lao PDR", "Maldives", "Mongolia", "Myanmar", "Nepal", "Pakistan", "Tajikistan", "Timor-Leste", "Uzbekistan") ~ "Asia",
Debtor %in% c("Afghanistan", "Yemen, Rep.") ~ "Middle East",
Debtor %in% c("Guyana", "Haiti", "Honduras", "Nicaragua") ~ "South & Central America",
Debtor %in% c("Dominica", "Grenada", "St. Lucia", "St. Vincent and the Grenadines") ~ "The Caribbeans",
Debtor %in% c("Fiji", "Papua New Guinea", "Samoa", "Solomon Islands", "Tonga", "Vanuatu") ~ "Pacific Islands",
Debtor %in% c("Kosovo", "Moldova") ~ "Europe"
))creditors_years <- selected_creditors %>% group_by(Creditor, year) %>% summarize(total_lending_bil = sum(Debt_Stocks)/10^9)## `summarise()` has grouped output by 'Creditor'. You can override using the `.groups` argument.ggplotly(ggplot(creditors_years) + geom_line(aes(x = year, y = total_lending_bil, color = Creditor)) + theme_minimal()) ggplotly(ggplot(creditors_years %>% filter(Creditor %in% c("China", "Japan", "United States", "United Kingdom", "France", "Saudi Arabia", "India"))) + geom_line(aes(x = year, y = total_lending_bil, color = Creditor)) + theme_minimal())## Very clear jump from China :0Networks
library(igraph)##
## Attaching package: 'igraph'## The following object is masked from 'package:plotly':
##
## groups## The following objects are masked from 'package:dplyr':
##
## as_data_frame, groups, union## The following objects are masked from 'package:purrr':
##
## compose, simplify## The following object is masked from 'package:tidyr':
##
## crossing## The following object is masked from 'package:tibble':
##
## as_data_frame## The following objects are masked from 'package:stats':
##
## decompose, spectrum## The following object is masked from 'package:base':
##
## unionlibrary(networkD3)##
## Attaching package: 'networkD3'## The following object is masked from 'package:highcharter':
##
## JSlinks_2019 <- debt_2019 %>% select(Debtor, Creditor, Debt_Stocks) %>% filter(Debt_Stocks != 0) %>% filter(!Creditor %in% c("Other Bilateral", "Other Multiple Lenders", "Other Multilaterals", "World", "Multiple Lenders", "Bondholders"))
links_2019 <- links_2019[, c(2, 1, 3)]
links_2019$Debt_Stocks <- links_2019$Debt_Stocks/10^9
debtors_2019 <- links_2019 %>% group_by(Debtor) %>% summarize(size = sum(Debt_Stocks))
debtors_2019$group <- "Debtor"
debtors_2019 <- debtors_2019 %>% rename(name = Debtor)
creditors_2019 <- links_2019 %>% group_by(Creditor) %>% summarize(size = sum(Debt_Stocks))
creditors_2019$group <- "Creditor"
creditors_2019 <- creditors_2019 %>% rename(name = Creditor)
nodes_2019 <- rbind(debtors_2019, creditors_2019)
nodes_2019 <- as.data.frame(nodes_2019)
links_2019 <- as.data.frame(links_2019)
nodes_2019$id <- 1:nrow(nodes_2019)
nodes_2019 <- nodes_2019 %>% mutate(region = case_when(
name %in% c("Angola", "Benin", "Burkina Faso", "Burundi", "Cabo Verde", "Cameroon", "Central African Republic", "Chad", "Comoros", "Congo, Dem. Rep.", "Congo, Rep.", "Cote d'Ivoire", "Djibouti", "Ethiopia", "Gambia, The", "Ghana", "Guinea", "Guinea-Bissau", "Kenya", "Lesotho", "Liberia", "Madagascar", "Malawi", "Mali", "Mauritania", "Mozambique", "Niger", "Nigeria", "Rwanda", "Sao Tome and Principe", "Senegal", "Sierra Leone", "Somalia", "Tanzania", "Togo", "Uganda", "Zambia", "Cote D`Ivoire, Republic Of", "Libya", "Mauritius", "South Africa") ~ "Africa",
name %in% c("Bangladesh", "Bhutan", "Cambodia", "Kyrgyz Republic", "Lao PDR", "Maldives", "Mongolia", "Myanmar", "Nepal", "Pakistan", "Tajikistan", "Timor-Leste", "Uzbekistan", "China", "Hong Kong", "India", "Japan", "Korea, Republic Of", "Kuwait", "Malaysia", "Singapore", "Sri Lanka", "Thailand") ~ "Asia",
name %in% c("Afghanistan", "Yemen, Rep.", "Bahrain", "Egypt", "Iran, Islamic Republic Of", "Israel", "Saudi Arabia", "United Arab Emirates") ~ "Middle East",
name %in% c("Guyana", "Haiti", "Honduras", "Nicaragua", "Brazil", "Venezuela, Republic Bolivarian") ~ "South & Central America",
name %in% c("Dominica", "Grenada", "St. Lucia", "St. Vincent and the Grenadines", "Bahamas", "Barbados", "St. Kitts And Nevis", "Trinidad & Tobago", "St. Vincent & The Grenadines") ~ "The Caribbeans",
name %in% c("Canada", "United States") ~ "North America",
name %in% c("Fiji", "Papua New Guinea", "Samoa", "Solomon Islands", "Tonga", "Vanuatu", "Australia") ~ "Oceania",
name %in% c("Kosovo", "Moldova", "Argentina", "Austria", "Belarus", "Belgium", "Czech Republic", "Denmark", "Finland", "France", "Germany, Fed.Rep. Of", "Greece", "Hungary", "Ireland", "Italy", "Netherlands", "Norway", "Poland", "Portugal", "Russian Federation", "Serbia", "Slovenia", "Spain", "Sweden", "Switzerland", "Turkey", "United Kingdom") ~ "Europe",
name %in% c("African Dev. Bank", "Asian Dev. Bank", "Inter-American Dev. Bank", "International Monetary Fund", "World Bank-IBRD", "World Bank-IDA") ~ "Organizations"
))
links_2019 <- left_join(links_2019, nodes_2019 %>% select(name, id), by = c("Creditor" = "name"))
links_2019 <- left_join(links_2019, nodes_2019 %>% select(name, id), by = c("Debtor" = "name"))
links_2019 <- links_2019 %>% rename(source = id.x, target = id.y)
links_2019$source <- links_2019$source - 1
links_2019$target <- links_2019$target - 1forceNetwork(Links = links_2019,
Nodes = nodes_2019,
Source = "source",
Target = "target",
Value = "Debt_Stocks",
NodeID = "name",
Nodesize = "size",
Group = "region",
opacity = 0.9, zoom = FALSE, legend = TRUE, arrows = TRUE, height = 500, width = 1000)Filtered for top creditors:
forceNetwork(Links = links_2019 %>% filter(Creditor %in% top_creditors),
Nodes = nodes_2019,
Source = "source",
Target = "target",
Value = "Debt_Stocks",
NodeID = "name",
Nodesize = "size",
Group = "region",
opacity = 0.9, zoom = TRUE, legend = TRUE, arrows = TRUE, height = 500, width = 1000)library(visNetwork)
nodes <- nodes_2019
nodes <- nodes %>% rename(label = name, value = size, status = group, group = region) %>% mutate(tooltip = label) %>% mutate(shape = ifelse(status == "Debtor", "circle", "triangle"))
edges <- links_2019
edges <- edges %>% rename(from = source, to = target, value = Debt_Stocks)
visNetwork(nodes, edges, height = "700px", width = "100%") %>%
visOptions(selectedBy = "group",
highlightNearest = TRUE,
nodesIdSelection = TRUE) %>%
visLegend(useGroups = TRUE, addNodes = data.frame(shape = "circle", color = c("darkgoldenrod2", "darkolivegreen4", "darkorange2", "aquamarine3", "pink", "cyan3", "darkorchid1", "darkgrey", "cornflowerblue"))) %>%
visPhysics(stabilization = FALSE) %>%
visEdges(arrows =list(to = list(),from= list()))
## I can't man